It remains unclear how humans are expected to recognize erroneous algorithmic predictions in production settings, where the true error in individual predictions is usually unknown.
Beyond global performance metrics, such as the error on a test set, many machine learning systems do not provide estimates of the reliability of individual predictions [1]}.
Additionally, the complex nature of many machine learning algorithms means that the user has little insight into how an algorithm has reached a prediction, which makes it even more difficult to assess whether or not the prediction is trustworthy [2]}.
